Complex SSD problems with several design parameters and several objectives could in theory be solved using benchmark multi-objective optimisation algorithms such as NSGA-II [1]}, robust implementations of which are freely available in statistical software such as R [2]}. However, these so-called `greedy' algorithms typically assume that evaluating any proposed solution to the problem is a very fast process, and consequently evaluate many thousands of solutions during the search. If these algorithms were applied to problems where evaluating solutions required computing an MC estimate of power, they would take an infeasibly long time to converge. Thus, if we are to extend simulation-based trial design to complex SSD problems, we require a more general framework employing more efficient optimisation algorithms.
